AI models
ERNIE-RNA
Training compute
2.1×10²¹ FLOP
Parameters
86M
Published
Mar 17, 2024
ERNIE-RNA is an AI model developed by Microsoft Research, Syngentech and Tsinghua University (United States and China), first published in March 2024. It works in the biology domain, on tasks such as protein or nucleotide language model (plm/nlm).
Training it took an estimated 2.1×10²¹ FLOP of compute (estimation method: hardware). The model has 86,000,000 parameters. It was trained on roughly 918M datapoints. Training ran on 24 NVIDIA Tesla V100 DGXS 32 GB for about 480 hours.
The reference paper has 20 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Microsoft Research, Syngentech, Tsinghua University
- Country of organization
- United States, China
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
- Training compute
- 2.1×10²¹ FLOP
- Compute estimation method
- Hardware
- Parameters
- 86,000,000
- Dataset size
- 918M
- Training hardware
- NVIDIA Tesla V100 DGXS 32 GB
- Chips used
- 24
- Training time
- 480 h
- Training power draw
- 11.9 kW
- Citations
- 20
- Epoch confidence
- Confident
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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